entry_point stringlengths 1 65 | original_triton_python_code stringlengths 208 619k | optimised_triton_code stringlengths 1.15k 275k | repo_name stringlengths 7 115 | module_name stringlengths 1 65 | synthetic bool 1
class | uuid int64 0 18.5k | licenses listlengths 1 6 | stars int64 0 19.8k | sha stringlengths 40 40 | repo_link stringlengths 72 180 |
|---|---|---|---|---|---|---|---|---|---|---|
GHMC | import torch
import torch.nn as nn
import torch.nn.functional as F
def _expand_binary_labels(labels, label_weights, label_channels):
bin_labels = labels.new_full((labels.size(0), label_channels), 0)
inds = torch.nonzero(labels >= 1).squeeze()
if inds.numel() > 0:
bin_labels[inds, labels[inds] - 1]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | AlphaLFC/mmdetection | GHMC | false | 4,849 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
Critic | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Critic(nn.Module):
"""Critic (Value) Model."""
def __init__(self, state_size, action_size, seed, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import numpy as np
import tor... | AnKra/deep-reinforcement-learning | Critic | false | 4,850 | [
"MIT"
] | 1 | fa906b0a3a21102b5085ce0c934185d2e50c3324 | https://github.com/AnKra/deep-reinforcement-learning/tree/fa906b0a3a21102b5085ce0c934185d2e50c3324 |
Actor | import torch
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
def hidden_init(layer):
fan_in = layer.weight.data.size()[0]
lim = 1.0 / np.sqrt(fan_in)
return -lim, lim
class Actor(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, f... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AnKra/deep-reinforcement-learning | Actor | false | 4,851 | [
"MIT"
] | 1 | fa906b0a3a21102b5085ce0c934185d2e50c3324 | https://github.com/AnKra/deep-reinforcement-learning/tree/fa906b0a3a21102b5085ce0c934185d2e50c3324 |
Network | import torch
import torch.nn as nn
class Network(nn.Module):
def __init__(self, input_shape, output_shape, n_features, **kwargs):
super(Network, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h1 = nn.Linear(n_input, n_features)
self._h2 = nn.Li... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AmmarFahmy/mushroom-rl | Network | false | 4,852 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 |
SmoothL1Loss | import functools
import torch
import torch.nn as nn
import torch.nn.functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are "none", "mean" and "sum".
Return:
Tensor: Reduced loss ten... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import functools
impor... | AlphaLFC/mmdetection | SmoothL1Loss | false | 4,853 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
outconv | import torch
import torch.nn as nn
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | AntarSidgi/LiverTumorSegmentation | outconv | false | 4,854 | [
"MIT"
] | 1 | 9e8b1182541e011dc9f14218276ee9cb736ce479 | https://github.com/AntarSidgi/LiverTumorSegmentation/tree/9e8b1182541e011dc9f14218276ee9cb736ce479 |
CriticNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class CriticNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super().__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_output)
nn.ini... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AmmarFahmy/mushroom-rl | CriticNetwork | false | 4,855 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 |
DiceLoss | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import collections
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing impor... | Alxaline/MONAI | DiceLoss | false | 4,856 | [
"Apache-2.0"
] | 1 | 6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | https://github.com/Alxaline/MONAI/tree/6b8fdf9db7f13ed7d88d605155a0463840abcbf2 |
Sum | import torch
import numpy as np
import torch.nn.functional as F
from torch import nn
from torch.autograd import Variable as Variable
class Sum(nn.Module):
def __init__(self, in_channels, in_features, out_channels, dropout=0.0):
"""
Create a Sum layer.
Args:
in_channels (int):... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
f... | AmurG/SPFlow | Sum | false | 4,857 | [
"Apache-2.0"
] | 1 | ab28dd4af9ed722ace69c6b290cf0a279bbda39e | https://github.com/AmurG/SPFlow/tree/ab28dd4af9ed722ace69c6b290cf0a279bbda39e |
QNetwork | import torch
import torch.nn.functional as F
import torch.nn as nn
class QNetwork(nn.Module):
"""Actor (Policy) Model."""
def __init__(self, state_size, action_size, seed, fc1_units=64,
fc2_units=64):
"""Initialize parameters and build model.
Params
======
state_si... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AmineKheldouni/Graphs-in-Machine-Learning | QNetwork | false | 4,858 | [
"MIT"
] | 1 | 003217495c624eaa33d44d679a0bc2164ca1f3d2 | https://github.com/AmineKheldouni/Graphs-in-Machine-Learning/tree/003217495c624eaa33d44d679a0bc2164ca1f3d2 |
GHMR | import torch
import torch.nn as nn
class GHMR(nn.Module):
"""GHM Regression Loss.
Details of the theorem can be viewed in the paper
"Gradient Harmonized Single-stage Detector"
https://arxiv.org/abs/1811.05181
Args:
mu (float): The parameter for the Authentic Smooth L1 loss.
bins ... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | AlphaLFC/mmdetection | GHMR | false | 4,859 | [
"Apache-2.0"
] | 1 | 45619c5b8aca0ca3e6ddc211210a8946c94694d8 | https://github.com/AlphaLFC/mmdetection/tree/45619c5b8aca0ca3e6ddc211210a8946c94694d8 |
ActorNetwork | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorNetwork(nn.Module):
def __init__(self, input_shape, output_shape, **kwargs):
super(ActorNetwork, self).__init__()
n_input = input_shape[-1]
n_output = output_shape[0]
self._h = nn.Linear(n_input, n_outpu... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AmmarFahmy/mushroom-rl | ActorNetwork | false | 4,860 | [
"MIT"
] | 1 | 2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 | https://github.com/AmmarFahmy/mushroom-rl/tree/2625ee7f64d5613b3b9fba00f0b7a39fece88ca5 |
LayerNorm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
assert_s... | AnonymousGFR/wbgan.pytorch | LayerNorm | false | 4,861 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 |
AdaIN | import math
import torch
import torch.nn as nn
from numpy import prod
def getLayerNormalizationFactor(x):
"""
Get He's constant for the given layer
https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/He_Delving_Deep_into_ICCV_2015_paper.pdf
"""
size = x.weight.size()
fan_in = pro... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Andribi/pytorch_GAN_zoo | AdaIN | false | 4,862 | [
"BSD-3-Clause"
] | 1 | b37c7268cbd4ec7dc61ba65a3ccf11af71247597 | https://github.com/Andribi/pytorch_GAN_zoo/tree/b37c7268cbd4ec7dc61ba65a3ccf11af71247597 |
CmapPafHead | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
import torch.nn
import torch.optim
assert_size_stride = ... | Anqi-nus/trtpose | CmapPafHead | false | 4,863 | [
"MIT"
] | 1 | 723ec95df8b8414b9289af90fbfbc98756792a21 | https://github.com/Anqi-nus/trtpose/tree/723ec95df8b8414b9289af90fbfbc98756792a21 |
QNetwork | import torch
import torch.nn.functional as F
from torch import nn
import torch.nn
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.constant_(m.bias, 0)
class QNetwork(nn.Module):
def __init__(self, num_inputs, num_actions, hidd... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
import t... | AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents | QNetwork | false | 4,864 | [
"MIT"
] | 1 | e7cfa4121542312de641792288f7487f86971c1e | https://github.com/AmmarFayad/Influence-based-Reinforcement-Learning-in-Intrinsically-motivated-Agents/tree/e7cfa4121542312de641792288f7487f86971c1e |
GeLU | import math
import torch
import torch.nn as nn
def gelu(x):
"""Implementation of the gelu activation function.
For information: OpenAI GPT's gelu is slightly different (and gives slightly different results):
0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.... | AsmitaBhat30/lxmert | GeLU | false | 4,865 | [
"MIT"
] | 1 | 90292dc36a25c04c4f76fe9119e3141d5dc05874 | https://github.com/AsmitaBhat30/lxmert/tree/90292dc36a25c04c4f76fe9119e3141d5dc05874 |
LayerNorm | import torch
import torch.nn as nn
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-05, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Param... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | AntiAegis/PyTorch-GAN | LayerNorm | false | 4,866 | [
"MIT"
] | 1 | 1cb951b3ad3a58b749c1802f84947b85f72c8367 | https://github.com/AntiAegis/PyTorch-GAN/tree/1cb951b3ad3a58b749c1802f84947b85f72c8367 |
SimpleCNN | import torch
import torch.nn as nn
from torch.nn import functional as F
class SimpleCNN(nn.Module):
def __init__(self):
super(SimpleCNN, self).__init__()
self.fc1 = nn.Linear(28 * 28, 500)
self.fc2 = nn.Linear(500, 256)
self.fc3 = nn.Linear(256, 10)
def forward(self, x):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | AnweshCR7/autonomous_greenhouse | SimpleCNN | false | 4,867 | [
"MIT"
] | 1 | a29cfe37d0152001d2544216ed65c3472f572b4e | https://github.com/AnweshCR7/autonomous_greenhouse/tree/a29cfe37d0152001d2544216ed65c3472f572b4e |
Pairer | import torch
import numpy as np
from torch import Tensor
from torch.functional import Tensor
from typing import Union
class Pairer(torch.nn.Module):
"""
To predict links between segments we will find all possible pairs and estimate the probability that they are linked.
We do this by creating a matrix whe... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_strided_cuda
@triton.j... | AxlAlm/SegNLP | Pairer | false | 4,868 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
SourceContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AngusGLChen/qg | SourceContextGate | false | 4,869 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
BothContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AngusGLChen/qg | BothContextGate | false | 4,870 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
TargetContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AngusGLChen/qg | TargetContextGate | false | 4,871 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
ContextGate | import torch
import torch.nn as nn
import torch.cuda
class ContextGate(nn.Module):
"""Implement up to the computation of the gate"""
def __init__(self, embeddings_size, decoder_size, attention_size,
output_size):
super(ContextGate, self).__init__()
input_size = embeddings_size + decod... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.cuda
assert_size_stride = torch._C._dynamo.gu... | AngusGLChen/qg | ContextGate | false | 4,872 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
SimpleCNN | import torch
import torch.nn.functional as F
class SimpleCNN(torch.nn.Module):
def __init__(self, in_ch=1, out_ch=3):
super(SimpleCNN, self).__init__()
self.conv1 = torch.nn.Conv2d(in_ch, out_ch, kernel_size=3, stride=1,
padding=1)
self.conv2 = torch.nn.Conv2d(out_ch, out_ch, ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Arjun-Arora/CS348B_project | SimpleCNN | false | 4,873 | [
"BSD-2-Clause"
] | 1 | 000ced8edbc3554db74db36ebcd76042d17398ee | https://github.com/Arjun-Arora/CS348B_project/tree/000ced8edbc3554db74db36ebcd76042d17398ee |
LayerNorm | import torch
import torch.nn as nn
import torch.optim
class LayerNorm(nn.Module):
"""Construct a layernorm module in the OpenAI style (epsilon inside the square root)."""
def __init__(self, n_state, e=1e-05):
super(LayerNorm, self).__init__()
self.g = nn.Parameter(torch.ones(n_state))
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
import torch.optim
assert_size_stride = torch._C._dynamo.... | Arvindkrishna1997/comet-dataset | LayerNorm | false | 4,874 | [
"Apache-2.0"
] | 1 | 2cb42a4aefdea6d0e81f544f94830d44730e9853 | https://github.com/Arvindkrishna1997/comet-dataset/tree/2cb42a4aefdea6d0e81f544f94830d44730e9853 |
ScaledDotProductAttention | import torch
import numpy as np
from torch import nn
class ScaledDotProductAttention(nn.Module):
""" Scaled Dot-Product Attention """
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.dropout = nn.Dropout(attn_dropout)
se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AutuanLiu/LeetCode2019 | ScaledDotProductAttention | false | 4,875 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 |
MyLayerNorm | import torch
import torch.nn as nn
class MyLayerNorm(nn.Module):
def __init__(self, input_dim):
super(MyLayerNorm, self).__init__()
self.gamma = nn.Parameter(torch.ones(input_dim))
if True or use_bias:
self.beta = nn.Parameter(torch.ones(input_dim))
def forward(self, x):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_... | Ar-Kareem/Sketch-RNN | MyLayerNorm | false | 4,876 | [
"MIT"
] | 1 | 350824040715ea281182de01bca467130f326566 | https://github.com/Ar-Kareem/Sketch-RNN/tree/350824040715ea281182de01bca467130f326566 |
ConvNet | import torch
import torch.nn as nn
class ConvNet(nn.Module):
"""Standard convolutional net for baseline
Architecture: 2 convolutional layers, 3 fully connected layers.
"""
def __init__(self):
super(ConvNet, self).__init__()
args = {'stride': 1, 'padding': 1}
self.conv1 = nn.Co... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | Allen-Z-4230/MoCo-CIFAR10 | ConvNet | false | 4,877 | [
"MIT"
] | 1 | b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 | https://github.com/Allen-Z-4230/MoCo-CIFAR10/tree/b2ade575b8ed1e05e32e4ec629acdfee55c8ff41 |
HS | import torch
import torch.nn as nn
class HS(nn.Module):
def __init__(self):
super(HS, self).__init__()
def forward(self, inputs):
clip = torch.clamp(inputs + 3, 0, 6) / 6
return inputs * clip
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
retur... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | BXuan694/basemodel-pytorch | HS | false | 4,878 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 |
ConditionalBatchNorm2d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.utils.data
from torch.nn import Parameter
def l2normalize(v, eps=0.0001):
return v / (v.norm() + eps)
class SpectralNorm(nn.Module):
def __init__(self, module, name='weight', power_iterations=1):
super(SpectralNorm, self).__in... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AnonymousGFR/wbgan.pytorch | ConditionalBatchNorm2d | false | 4,879 | [
"MIT"
] | 1 | d75cb6599852e901df0136db87520e3314f8ca71 | https://github.com/AnonymousGFR/wbgan.pytorch/tree/d75cb6599852e901df0136db87520e3314f8ca71 |
GlobalAttention | import torch
import torch.nn as nn
import torch.cuda
def aeq(base, *rest):
""" Assert the first arg equals to each of the rest."""
for a in rest[:]:
assert a == base, 'base(' + str(base
) + ") doesn't equals to each of " + str(rest)
class Bottle(nn.Module):
def forward(self, input):... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | AngusGLChen/qg | GlobalAttention | false | 4,880 | [
"MIT"
] | 1 | 3ebc5b94348a4c313829a6c71705fbc9dadd8181 | https://github.com/AngusGLChen/qg/tree/3ebc5b94348a4c313829a6c71705fbc9dadd8181 |
AdditiveAttention | import torch
from torch import Tensor
from torch.functional import Tensor
import torch.nn as nn
class AdditiveAttention(nn.Module):
"""
Originally from:
https://arxiv.org/pdf/1409.0473v5.pdf
Also referenced to as Content Based Attention:
https://arxiv.org/pdf/1506.03134v1.pdf
Attenti... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | AxlAlm/SegNLP | AdditiveAttention | false | 4,881 | [
"Apache-2.0"
] | 1 | 89b8d077952397dfcea089376b373b117bcf6a65 | https://github.com/AxlAlm/SegNLP/tree/89b8d077952397dfcea089376b373b117bcf6a65 |
LayerNorm | import torch
class LayerNorm(torch.nn.Module):
def __init__(self, dimensions, eps: 'float'=1e-06) ->None:
super().__init__()
self.gamma = torch.nn.Parameter(torch.ones(dimensions))
self.beta = torch.nn.Parameter(torch.zeros(dimensions))
self.eps = eps
def forward(self, tensor... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_c... | AutuanLiu/LeetCode2019 | LayerNorm | false | 4,882 | [
"MIT"
] | 1 | 8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 | https://github.com/AutuanLiu/LeetCode2019/tree/8efc7c5475fd888f7d86c3b08a3c1c9e55c1ac30 |
Dnn_net_Loss | import torch
import torch.utils.data
class Dnn_net_Loss(torch.nn.Module):
def __init__(self):
super(Dnn_net_Loss, self).__init__()
def forward(self, model_output, targ_input):
criterion = torch.nn.MSELoss(reduction='none')
criterion
targ_input = torch.cat((targ_input[:, :, 0]... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
e... | BaiYunLiu/newPLC | Dnn_net_Loss | false | 4,883 | [
"BSD-3-Clause"
] | 1 | 18245a14648bc28b7269ea1d6e444ca6021ac8d2 | https://github.com/BaiYunLiu/newPLC/tree/18245a14648bc28b7269ea1d6e444ca6021ac8d2 |
Similarity | import torch
import torch.nn as nn
class Similarity(nn.Module):
"""
Dot product or cosine similarity
"""
def __init__(self, temp):
super().__init__()
self.temp = temp
self.cos = nn.CosineSimilarity(dim=-1)
def forward(self, x, y):
return self.cos(x, y) / self.temp... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as nn
assert... | BDBC-KG-NLP/MixCSE_AAAI2022 | Similarity | false | 4,884 | [
"MIT"
] | 1 | 884145e24a5258c044fedb658df9999f012df875 | https://github.com/BDBC-KG-NLP/MixCSE_AAAI2022/tree/884145e24a5258c044fedb658df9999f012df875 |
VAE | import torch
import torch.nn as nn
import torch.utils.data
from torch.nn import functional as F
class VAE(nn.Module):
def __init__(self, n_features=24, z_dim=15):
super(VAE, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(... | import torch
from torch import device
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | VAE | false | 4,885 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 |
Attention | import math
import torch
from torch import nn
class Attention(nn.Module):
"""A generic attention module for a decoder in seq2seq"""
def __init__(self, dim, use_tanh=False, C=10):
super(Attention, self).__init__()
self.use_tanh = use_tanh
self.project_query = nn.Linear(dim, dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import math
from to... | BCHoagland/attention-learn-to-route | Attention | false | 4,886 | [
"MIT"
] | 1 | c411289c3b42be5b9c89240f665a029dfc51e034 | https://github.com/BCHoagland/attention-learn-to-route/tree/c411289c3b42be5b9c89240f665a029dfc51e034 |
ConvLayer | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
assert_size_s... | Bartolo1024/ignite | ConvLayer | false | 4,887 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
AE_4D | import torch
import torch.nn as nn
import torch.utils.data
class AE_4D(nn.Module):
def __init__(self, n_features=4):
super(AE_4D, self).__init__()
self.en1 = nn.Linear(n_features, 200)
self.en2 = nn.Linear(200, 100)
self.en3 = nn.Linear(100, 50)
self.en4 = nn.Linear(50, 3)... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow | AE_4D | false | 4,888 | [
"Apache-2.0"
] | 1 | 772ba547c2b7d5d90e79382bf4d8a50e4d733210 | https://github.com/Autoencoders-compression-anomaly/Various-AEs-Compression-Tensorflow/tree/772ba547c2b7d5d90e79382bf4d8a50e4d733210 |
ActorMARL | import torch
import torch.nn as nn
import torch.nn.functional as F
class ActorMARL(nn.Module):
def __init__(self, dim_observation, dim_action):
super(ActorMARL, self).__init__()
self.FC1 = nn.Linear(dim_observation, 500)
self.FC2 = nn.Linear(500, 128)
self.FC3 = nn.Linear(128, dim... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | BIT-UAV-JJJ/ElegantRL | ActorMARL | false | 4,889 | [
"Apache-2.0"
] | 1 | 5ce5c1030949bb862d0d56b0e78a9a1f47efe63a | https://github.com/BIT-UAV-JJJ/ElegantRL/tree/5ce5c1030949bb862d0d56b0e78a9a1f47efe63a |
eSEModule | import torch
import torch.nn as nn
import torch.nn.functional as F
class Hsigmoid(nn.Module):
def __init__(self, inplace=True):
super(Hsigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return F.relu6(x + 3.0, inplace=self.inplace) / 6.0
class eSEModule(nn.Modul... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | BXuan694/basemodel-pytorch | eSEModule | false | 4,890 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 |
Net | import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self, n_classes):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 *... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | ArWeHei/edflow | Net | false | 4,891 | [
"MIT"
] | 1 | 3383cfbc42a43e906bc7781ad05714fd4fc9616e | https://github.com/ArWeHei/edflow/tree/3383cfbc42a43e906bc7781ad05714fd4fc9616e |
SE | import torch
import torch.nn as nn
import torch.nn.functional as F
class SE(nn.Module):
"""Squeeze-and-Excitation block."""
def __init__(self, in_planes, se_planes):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_planes, se_planes, kernel_size=1, bias=True)
self.se2 = nn.Conv2d(se... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | BXuan694/basemodel-pytorch | SE | false | 4,892 | [
"MIT"
] | 1 | a36c96904580be902e323db17eebbe2ea1f54176 | https://github.com/BXuan694/basemodel-pytorch/tree/a36c96904580be902e323db17eebbe2ea1f54176 |
Actor | import torch
import torch.nn as nn
import torch.nn.functional as F
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, action_dim)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Barisimre/TD3-Generative | Actor | false | 4,893 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 |
GeneralizedDiceLoss | import collections
import torch
import warnings
from typing import Optional
from typing import Union
from typing import Any
from typing import Callable
from typing import Tuple
import torch.nn
from torch.nn.modules.loss import _Loss
from enum import Enum
import collections.abc
def issequenceiterable(obj: 'Any') ->boo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice
import collections
from typi... | Alxaline/MONAI | GeneralizedDiceLoss | false | 4,894 | [
"Apache-2.0"
] | 1 | 6b8fdf9db7f13ed7d88d605155a0463840abcbf2 | https://github.com/Alxaline/MONAI/tree/6b8fdf9db7f13ed7d88d605155a0463840abcbf2 |
Critic | import torch
import torch.nn as nn
import torch.nn.functional as F
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim + action_dim, 256)
self.l2 = nn.Linear(256, 256)
self.l3 = nn.Linear(256, 1)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | Barisimre/TD3-Generative | Critic | false | 4,895 | [
"MIT"
] | 1 | 434419b020b88010f09f194c40feac1d420b2086 | https://github.com/Barisimre/TD3-Generative/tree/434419b020b88010f09f194c40feac1d420b2086 |
DNNnet | import torch
import torch.utils.data
class DNNnet(torch.nn.Module):
def __init__(self, n_layer, n_in_channel, n_out_channel):
super(DNNnet, self).__init__()
self.n_layer = n_layer
self.fc_layers = torch.nn.ModuleList()
self.act_func = torch.nn.Sigmoid()
start_layer = torch... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | BaiYunLiu/newPLC | DNNnet | false | 4,896 | [
"BSD-3-Clause"
] | 1 | 18245a14648bc28b7269ea1d6e444ca6021ac8d2 | https://github.com/BaiYunLiu/newPLC/tree/18245a14648bc28b7269ea1d6e444ca6021ac8d2 |
SkipLastTargetChannelWrapper | import torch
import torch.nn as nn
from torch.nn import MSELoss
class SkipLastTargetChannelWrapper(nn.Module):
"""
Loss wrapper which removes additional target channel
"""
def __init__(self, loss, squeeze_channel=False):
super(SkipLastTargetChannelWrapper, self).__init__()
self.loss =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | BioTrillion/pytorch-3dunet | SkipLastTargetChannelWrapper | false | 4,897 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 |
WeightBCE | import torch
from torch import Tensor
from torch import nn
class WeightBCE(nn.Module):
def __init__(self, epsilon: 'float'=1e-08) ->None:
super(WeightBCE, self).__init__()
self.epsilon = epsilon
def forward(self, x: 'Tensor', label: 'Tensor', weight: 'Tensor') ->Tensor:
"""
:... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | BetterRaven/Transfer-Learning_vscode | WeightBCE | false | 4,898 | [
"MIT"
] | 1 | 90c9bce630f54fd2322cce8fab5fe1d074ff141c | https://github.com/BetterRaven/Transfer-Learning_vscode/tree/90c9bce630f54fd2322cce8fab5fe1d074ff141c |
CNN | import torch
from torch import nn
import torch.nn.functional as F
class CNN(torch.nn.Module):
"""Basic CNN architecture."""
def __init__(self, in_channels=1):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)
self.conv2 = nn.Conv2d(64, 128, 6, 2)
self.c... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch import nn
assert_s... | AxelBohm/cleverhans | CNN | false | 4,899 | [
"MIT"
] | 1 | 35f44d686fa24a8d3a30218dc9ad2617859afbf0 | https://github.com/AxelBohm/cleverhans/tree/35f44d686fa24a8d3a30218dc9ad2617859afbf0 |
Policy | import torch
import torch.nn as nn
import torch.nn.functional as F
class Policy(nn.Module):
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
self.saved_log_probs = []
self.rewards = []
def forward(sel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bartolo1024/ignite | Policy | false | 4,900 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
MAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | MAB | false | 4,901 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
SAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | SAB | false | 4,902 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
PointLoss | import torch
import torch.nn.parallel
import torch.utils.data
import torch.nn as nn
def array2samples_distance(array1, array2):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is th... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn.parallel
import torch.utils.data
import torch.nn as nn
assert_size_stride... | AndyYuanC/VegPN | PointLoss | false | 4,903 | [
"MIT"
] | 1 | eb981d62ad854d3ca607240cc431a0870c1e95ba | https://github.com/AndyYuanC/VegPN/tree/eb981d62ad854d3ca607240cc431a0870c1e95ba |
ContrastiveLoss | import torch
import torch.nn as nn
class ContrastiveLoss(nn.Module):
"""
Contrastive loss function.
Based on: http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
Loss is proportional to square distance when inputs are of the same type, and proportional to
the square of margin - dista... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
emp... | BrunoKM/rhoana_graph_tools | ContrastiveLoss | false | 4,904 | [
"MIT"
] | 1 | 7150f4bc6337ecf51dd9123cf03561a57d655160 | https://github.com/BrunoKM/rhoana_graph_tools/tree/7150f4bc6337ecf51dd9123cf03561a57d655160 |
ResidualBlock | import torch
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = kernel_size // 2
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bartolo1024/ignite | ResidualBlock | false | 4,905 | [
"BSD-3-Clause"
] | 1 | b087fef0bc5f97cda415c1c56f1cd589383c54be | https://github.com/Bartolo1024/ignite/tree/b087fef0bc5f97cda415c1c56f1cd589383c54be |
WeightedSmoothL1Loss | import torch
import torch.nn as nn
class WeightedSmoothL1Loss(nn.SmoothL1Loss):
def __init__(self, threshold, initial_weight, apply_below_threshold=True):
super().__init__(reduction='none')
self.threshold = threshold
self.apply_below_threshold = apply_below_threshold
self.weight =... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | BioTrillion/pytorch-3dunet | WeightedSmoothL1Loss | false | 4,906 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 |
BCEDiceLoss | import torch
import torch.nn as nn
def flatten(tensor):
"""Flattens a given tensor such that the channel axis is first.
The shapes are transformed as follows:
(N, C, D, H, W) -> (C, N * D * H * W)
"""
C = tensor.size(1)
axis_order = (1, 0) + tuple(range(2, tensor.dim()))
transposed = te... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
import torc... | BioTrillion/pytorch-3dunet | BCEDiceLoss | false | 4,907 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 |
BatchLinear | import torch
import torch.nn as nn
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited from `MetaModule` are fully compa... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | Bunnycakes62/SIREN | BatchLinear | false | 4,908 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b |
PMA | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | PMA | false | 4,909 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
ISAB | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class MAB(nn.Module):
def __init__(self, dim_Q, dim_K, dim_V, num_heads, ln=False):
super(MAB, self).__init__()
self.dim_V = dim_V
self.num_heads = num_heads
self.fc_q = nn.Linear(dim_Q, dim_V)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Behrouz-Babaki/NCG4CVRP | ISAB | false | 4,910 | [
"MIT"
] | 1 | 87d63366c0b461f44ce8e982159a1e207af77b44 | https://github.com/Behrouz-Babaki/NCG4CVRP/tree/87d63366c0b461f44ce8e982159a1e207af77b44 |
TimeEncode | import torch
import numpy as np
class TimeEncode(torch.nn.Module):
def __init__(self, dimension):
super(TimeEncode, self).__init__()
self.dimension = dimension
self.w = torch.nn.Linear(1, dimension)
self.w.weight = torch.nn.Parameter(torch.from_numpy(1 / 10 ** np.
lins... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import numpy ... | Blidge/tgn-caw-main | TimeEncode | false | 4,911 | [
"Apache-2.0"
] | 1 | 7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee | https://github.com/Blidge/tgn-caw-main/tree/7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee |
CmapPafHeadAttention | import torch
import torch.utils.data
import torch.nn
import torch.optim
class UpsampleCBR(torch.nn.Sequential):
def __init__(self, input_channels, output_channels, count=1, num_flat=0):
layers = []
for i in range(count):
if i == 0:
inch = input_channels
els... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.utils.... | Anqi-nus/trtpose | CmapPafHeadAttention | false | 4,912 | [
"MIT"
] | 1 | 723ec95df8b8414b9289af90fbfbc98756792a21 | https://github.com/Anqi-nus/trtpose/tree/723ec95df8b8414b9289af90fbfbc98756792a21 |
MMD | import torch
from torch import nn
class MMD(nn.Module):
def __init__(self):
super().__init__()
def _guassian_kernel(self, source, target, kernel_mul=2.0, kernel_num=5,
fix_sigma=None):
n_samples = int(source.size()[0]) + int(target.size()[0])
total = torch.cat([source, target... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_... | BetterRaven/Transfer-Learning_vscode | MMD | false | 4,913 | [
"MIT"
] | 1 | 90c9bce630f54fd2322cce8fab5fe1d074ff141c | https://github.com/BetterRaven/Transfer-Learning_vscode/tree/90c9bce630f54fd2322cce8fab5fe1d074ff141c |
MergeLayer | import torch
class MergeLayer(torch.nn.Module):
def __init__(self, dim1, dim2, dim3, dim4):
super().__init__()
self.fc1 = torch.nn.Linear(dim1 + dim2, dim3)
self.fc2 = torch.nn.Linear(dim3, dim4)
self.act = torch.nn.ReLU()
torch.nn.init.xavier_normal_(self.fc1.weight)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Blidge/tgn-caw-main | MergeLayer | false | 4,914 | [
"Apache-2.0"
] | 1 | 7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee | https://github.com/Blidge/tgn-caw-main/tree/7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee |
IrisNet | import torch
import torch.nn.functional as F
import torch.nn as nn
class IrisNet(nn.Module):
def __init__(self):
super(IrisNet, self).__init__()
self.fc1 = nn.Linear(4, 100)
self.fc2 = nn.Linear(100, 100)
self.fc3 = nn.Linear(100, 3)
self.softmax = nn.Softmax(dim=1)
d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | Bhaskarkvvsr/cortex | IrisNet | false | 4,915 | [
"Apache-2.0"
] | 1 | f569791613ea8b8cff226c3585839d37b9b6a5b5 | https://github.com/Bhaskarkvvsr/cortex/tree/f569791613ea8b8cff226c3585839d37b9b6a5b5 |
Sine | import torch
import torch.nn as nn
class Sine(nn.Module):
def __init(self):
super().__init__()
def forward(self, input):
return torch.sin(5 * input)
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert... | Bunnycakes62/SIREN | Sine | false | 4,916 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b |
MetaBilinear | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class MetaModule(nn.Module):
"""
Base class for PyTorch meta-learning modules. These modules accept an
additional argument `params` in their `forward` method.
Notes
-----
Objects inherited f... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
reinterpret_tensor = torch._C._dynamo.guards._reinterp... | Bunnycakes62/SIREN | MetaBilinear | false | 4,917 | [
"MIT"
] | 1 | 87c2c9e28411fd6a83d1d0d1bc5141cce30e646b | https://github.com/Bunnycakes62/SIREN/tree/87c2c9e28411fd6a83d1d0d1bc5141cce30e646b |
GlobalAvgPool2d | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class GlobalAvgPool2d(nn.Module):
def __init__(self):
"""Global average pooling over the input's spatial dimensions"""
super(GlobalAvgPool2d, self).__init__()
def forward(self, inputs):
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.asser... | CFengFeng/face-nn | GlobalAvgPool2d | false | 4,918 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
LinearPool | import torch
import torch.nn as nn
class LinearPool(nn.Module):
def __init__(self):
super(LinearPool, self).__init__()
def forward(self, feat_map):
"""
Arguments:
feat_map(Tensor): tensor with shape (N, C, H, W)
return(Tensor): tensor with shape (N, C, 1, 1)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | C3-ASV-Team/torchxrayvision | LinearPool | false | 4,919 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
MLP | import torch
class MLP(torch.nn.Module):
def __init__(self, dim, drop=0.3):
super().__init__()
self.fc_1 = torch.nn.Linear(dim, 80)
self.fc_2 = torch.nn.Linear(80, 10)
self.fc_3 = torch.nn.Linear(10, 1)
self.act = torch.nn.ReLU()
self.dropout = torch.nn.Dropout(p=d... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C... | Blidge/tgn-caw-main | MLP | false | 4,920 | [
"Apache-2.0"
] | 1 | 7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee | https://github.com/Blidge/tgn-caw-main/tree/7a58f22bc7d9f1e2f6e9cbb1a60a18aed81071ee |
ExpPool | import torch
import torch.nn as nn
class ExpPool(nn.Module):
def __init__(self):
super(ExpPool, self).__init__()
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(Tensor): tensor with shape (N, C, H, W)
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | C3-ASV-Team/torchxrayvision | ExpPool | false | 4,921 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
FrameMaxPool | import torch
import torch.nn as nn
class FrameMaxPool(nn.Module):
def __init__(self, input_size, hidden_size, stride):
super(FrameMaxPool, self).__init__()
self.vis_conv = nn.Conv1d(input_size, hidden_size, 1, 1)
self.max_pool = nn.MaxPool1d(stride)
def forward(self, visual_input):
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | CFM-MSG/Code_LEORN | FrameMaxPool | false | 4,922 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
GCN | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class GraphConvolution(nn.Module):
"""
Simple GCN layer, similar to https://arxiv.org/abs/1609.02907
"""
def __init__(self, in_features, out_features, bias=True):
super(Grap... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import math
import torch.nn a... | BrunoKM/rhoana_graph_tools | GCN | false | 4,923 | [
"MIT"
] | 1 | 7150f4bc6337ecf51dd9123cf03561a57d655160 | https://github.com/BrunoKM/rhoana_graph_tools/tree/7150f4bc6337ecf51dd9123cf03561a57d655160 |
SSRLayer | import torch
import torch.nn as nn
class SSRLayer(nn.Module):
def __init__(self):
super(SSRLayer, self).__init__()
def forward(self, x):
a = x[0][:, :, 0] * 0
b = x[0][:, :, 0] * 0
c = x[0][:, :, 0] * 0
s1 = 3
s2 = 3
s3 = 3
lambda_d = 1
... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | C3Imaging/SyntheticHeadPose | SSRLayer | false | 4,924 | [
"MIT"
] | 1 | b139aeda41ace2a07138705a4997d2ea65cb11a6 | https://github.com/C3Imaging/SyntheticHeadPose/tree/b139aeda41ace2a07138705a4997d2ea65cb11a6 |
mfm | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, mode=1):
"""
mfm
:param in_channels: in channel
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CFengFeng/face-nn | mfm | false | 4,925 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
LogSumExpPool | import torch
import torch.nn as nn
class LogSumExpPool(nn.Module):
def __init__(self, gamma):
super(LogSumExpPool, self).__init__()
self.gamma = gamma
def forward(self, feat_map):
"""
Numerically stable implementation of the operation
Arguments:
feat_map(T... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
import torch.nn as nn
... | C3-ASV-Team/torchxrayvision | LogSumExpPool | false | 4,926 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
Log_Cosh_Loss | import torch
class Log_Cosh_Loss(torch.nn.Module):
def forward(self, logits, labels):
return torch.mean(torch.log(torch.cosh(labels - logits)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def get_init_inputs():
return [[], {}]
| import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | CODEJIN/RHRNet | Log_Cosh_Loss | false | 4,927 | [
"MIT"
] | 1 | 71bd9d40a9951a7beabe9c3e802e74af22dd405d | https://github.com/CODEJIN/RHRNet/tree/71bd9d40a9951a7beabe9c3e802e74af22dd405d |
ExtResNetBlock | import torch
import torch.nn as nn
def conv3d(in_channels, out_channels, kernel_size, bias, padding):
return nn.Conv3d(in_channels, out_channels, kernel_size, padding=
padding, bias=bias)
def create_conv(in_channels, out_channels, kernel_size, order, num_groups,
padding):
"""
Create a list o... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
import torch.nn as ... | BioTrillion/pytorch-3dunet | ExtResNetBlock | false | 4,928 | [
"MIT"
] | 1 | 217781197dd94211ee7fe5d53a8b404f0b8391a6 | https://github.com/BioTrillion/pytorch-3dunet/tree/217781197dd94211ee7fe5d53a8b404f0b8391a6 |
CAModule | import torch
import torch.nn as nn
class CAModule(nn.Module):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in:
*Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507*
code reference:
https://github.com/kobiso/CBAM-keras/blob/master/models/attention_module.py
"""
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
assert_... | C3-ASV-Team/torchxrayvision | CAModule | false | 4,929 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
PcamPool | import torch
import torch.nn as nn
class PcamPool(nn.Module):
def __init__(self):
super(PcamPool, self).__init__()
def forward(self, feat_map, logit_map):
assert logit_map is not None
prob_map = torch.sigmoid(logit_map)
weight_map = prob_map / prob_map.sum(dim=2, keepdim=True... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_st... | C3-ASV-Team/torchxrayvision | PcamPool | false | 4,930 | [
"Apache-2.0"
] | 1 | 7e53f0606986562f17a1ffd9f31d006756eff78d | https://github.com/C3-ASV-Team/torchxrayvision/tree/7e53f0606986562f17a1ffd9f31d006756eff78d |
TrajectoryPredictor | import torch
import torch.nn as nn
class TrajectoryPredictor(nn.Module):
def __init__(self, pose_size, trajectory_size, hidden_size):
super(TrajectoryPredictor, self).__init__()
self.lp = nn.Linear(hidden_size, pose_size)
self.fc = nn.Linear(pose_size + hidden_size, trajectory_size)
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
assert_size_stride = torch._C._dynamo.guards.assert_size_s... | CMU-MultiComp-Lab/language2pose | TrajectoryPredictor | false | 4,931 | [
"MIT"
] | 1 | b32199ae5b2b80087411504afef384e0fa689d04 | https://github.com/CMU-MultiComp-Lab/language2pose/tree/b32199ae5b2b80087411504afef384e0fa689d04 |
ResidualBlock | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, mode=1):
"""
mfm
:param in_channels: in channel
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CFengFeng/face-nn | ResidualBlock | false | 4,932 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
CoralLayer | import torch
class CoralLayer(torch.nn.Module):
""" Implements CORAL layer described in
Cao, Mirjalili, and Raschka (2020)
*Rank Consistent Ordinal Regression for Neural Networks
with Application to Age Estimation*
Pattern Recognition Letters, https://doi.org/10.1016/j.patrec.2020.11.008
... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cu... | CHNxindong/corn-ordinal-neuralnet | CoralLayer | false | 4,933 | [
"MIT"
] | 1 | 7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 | https://github.com/CHNxindong/corn-ordinal-neuralnet/tree/7f8a45614cb6488e9c019c5e9d3a5aee0d714e94 |
ConvNorm | import torch
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
assert kernel_size % 2 ... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
assert_size_stride = torch._C._dynamo.guards.assert_size... | CODEJIN/TacoSinger | ConvNorm | false | 4,934 | [
"MIT"
] | 1 | af58a8f4e8b20e8817990f28a3ba22168c853655 | https://github.com/CODEJIN/TacoSinger/tree/af58a8f4e8b20e8817990f28a3ba22168c853655 |
group | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
class mfm(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1,
padding=1, mode=1):
"""
mfm
:param in_channels: in channel
:param out_channel... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.nn as nn
import ... | CFengFeng/face-nn | group | false | 4,935 | [
"MIT"
] | 1 | a76a689774b5101959d3c5b8a04898ae82c7bfc2 | https://github.com/CFengFeng/face-nn/tree/a76a689774b5101959d3c5b8a04898ae82c7bfc2 |
Metaloss | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
class Metaloss(nn.Module):
def __init__(self):
super(Metaloss, self).__init__()
def forward(self, x):
return x.mean(0).sum()
def get_inputs():
return [torch.rand([4, 4, 4, 4])]
def get_init_inpu... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | CQUlearningsystemgroup/LearningToBinarize | Metaloss | false | 4,936 | [
"MIT"
] | 1 | 1ecad897145af65ff52323bf2ec64a2154dc87d6 | https://github.com/CQUlearningsystemgroup/LearningToBinarize/tree/1ecad897145af65ff52323bf2ec64a2154dc87d6 |
BinaryActivation | import torch
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
class BinaryActivation(nn.Module):
def __init__(self):
super(BinaryActivation, self).__init__()
def forward(self, x):
out_forward = torch.sign(x)
mask1 = x < -1
mask2 = x < 0
mas... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils
import torch.utils.data.distributed
assert_size_stride = torch._C._dynamo.guards.assert_size_stride... | CQUlearningsystemgroup/LearningToBinarize | BinaryActivation | false | 4,937 | [
"MIT"
] | 1 | 1ecad897145af65ff52323bf2ec64a2154dc87d6 | https://github.com/CQUlearningsystemgroup/LearningToBinarize/tree/1ecad897145af65ff52323bf2ec64a2154dc87d6 |
LocationLayer | import torch
import torch.nn as nn
import torch.utils.data
class ConvNorm(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=1, stride=1,
padding=None, dilation=1, bias=True, w_init_gain='linear'):
super(ConvNorm, self).__init__()
if padding is None:
a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
import torch.utils.data
assert_size_stride = torch._C._dyn... | CODEJIN/TacoSinger | LocationLayer | false | 4,938 | [
"MIT"
] | 1 | af58a8f4e8b20e8817990f28a3ba22168c853655 | https://github.com/CODEJIN/TacoSinger/tree/af58a8f4e8b20e8817990f28a3ba22168c853655 |
PostSynthesisProcessing | import torch
class PostSynthesisProcessing(torch.nn.Module):
def __init__(self):
super().__init__()
self.min_value = -1
self.max_value = 1
def forward(self, synthesized_image):
synthesized_image = (synthesized_image - self.min_value
) * torch.tensor(255).float() /... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | CSID-DGU/-2020-1-OSSP1-ninetynine-2 | PostSynthesisProcessing | false | 4,939 | [
"MIT"
] | 1 | b1824254882eeea0ee44e4e60896b72c51ef1d2c | https://github.com/CSID-DGU/-2020-1-OSSP1-ninetynine-2/tree/b1824254882eeea0ee44e4e60896b72c51ef1d2c |
LogCoshLoss | import torch
class LogCoshLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
loss = true - pred
return torch.mean(torch.log(torch.cosh(loss + 1e-12)))
def get_inputs():
return [torch.rand([4, 4, 4, 4]), torch.rand([4, 4, 4, 4])]
def g... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import libdevice, math as tl_math
assert_size... | CSID-DGU/-2020-1-OSSP1-ninetynine-2 | LogCoshLoss | false | 4,940 | [
"MIT"
] | 1 | b1824254882eeea0ee44e4e60896b72c51ef1d2c | https://github.com/CSID-DGU/-2020-1-OSSP1-ninetynine-2/tree/b1824254882eeea0ee44e4e60896b72c51ef1d2c |
LatentLoss | import torch
class L1Loss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
return torch.mean(torch.abs(true - pred))
class LogCoshLoss(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, true, pred):
lo... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torc... | CSID-DGU/-2020-1-OSSP1-ninetynine-2 | LatentLoss | false | 4,941 | [
"MIT"
] | 1 | b1824254882eeea0ee44e4e60896b72c51ef1d2c | https://github.com/CSID-DGU/-2020-1-OSSP1-ninetynine-2/tree/b1824254882eeea0ee44e4e60896b72c51ef1d2c |
MaxBlock | import torch
import torch.utils.data
import torch.nn as nn
class MaxBlock(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.proj = nn.Linear(in_dim, out_dim)
def forward(self, x):
xm, _ = x.max(dim=1, keepdim=True)
x = self.proj(x - xm)
return x... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
import torch.utils.data
impor... | CS236G/pcgan | MaxBlock | false | 4,942 | [
"MIT"
] | 1 | e1ac013a087617f93c14347428a0d234d6d2a012 | https://github.com/CS236G/pcgan/tree/e1ac013a087617f93c14347428a0d234d6d2a012 |
AttendedTextEncoding | import torch
import torch.nn as nn
class AttendedTextEncoding(nn.Module):
def __init__(self, hidden_size):
super(AttendedTextEncoding, self).__init__()
self.sentence_linear = nn.Linear(hidden_size, hidden_size)
self.att_linear1 = nn.Linear(hidden_size * 2, hidden_size // 2)
self.a... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.... | CFM-MSG/Code_LEORN | AttendedTextEncoding | false | 4,943 | [
"MIT"
] | 1 | fabea1e1ded973a4db692e51e2df442bde55f626 | https://github.com/CFM-MSG/Code_LEORN/tree/fabea1e1ded973a4db692e51e2df442bde55f626 |
Flatten | import torch
from torch import nn
class Flatten(nn.Module):
def __init__(self):
super(Flatten, self).__init__()
def forward(self, x):
"""
Arguments:
x: a float tensor with shape [batch_size, c, h, w].
Returns:
a float tensor with shape [batch_size, c*h... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._dynamo.guards._empty_str... | CTPLab/IID_representation_learning | Flatten | false | 4,944 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
ResConvGLU | import math
import torch
class Conv1d(torch.nn.Conv1d):
def __init__(self, *args, **kwargs):
super(Conv1d, self).__init__(*args, **kwargs)
def reset_parameters(self):
torch.nn.init.kaiming_normal_(self.weight, nonlinearity='relu')
if self.bias is not None:
torch.nn.init.z... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime.triton_helpers import libdevice
assert_size_stride ... | CODEJIN/PWGAN_Torch | ResConvGLU | false | 4,945 | [
"MIT"
] | 1 | 9bef273a55d1fa24575633d6473b304418e93374 | https://github.com/CODEJIN/PWGAN_Torch/tree/9bef273a55d1fa24575633d6473b304418e93374 |
Discriminator | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader as DataLoader
class Discriminator(nn.Module):
def __init__(self, in_size, hidden_size):
super(Discriminator, self).__init__()
self.L1 = nn.Linear(in_size, hidden_size)
self.L2 = nn.L... | import torch
from torch._inductor.select_algorithm import extern_kernels
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.nn as nn
from torch.utils.data import DataLoader as DataLoader
asse... | COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question | Discriminator | false | 4,946 | [
"MIT"
] | 1 | 7e2e632189a3669397f67efa99c8de4924967968 | https://github.com/COMP6248-Reproducability-Challenge/Reproducible-Or-Not-Reproducible-That-Is-The-Question/tree/7e2e632189a3669397f67efa99c8de4924967968 |
Scale | import torch
import torch.utils.data
from torch import nn
class Scale(nn.Module):
def __init__(self, init_value=1.0):
super(Scale, self).__init__()
self.scale = nn.Parameter(torch.FloatTensor([init_value]))
def forward(self, input):
return input * self.scale
def get_inputs():
r... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
import torch.utils.data
from torch import nn
assert_size_stride = torch._C._dynamo.guards.assert_size_stride
empty_strided_cuda = torch._C._... | CV-Rookie/EmbedMask | Scale | false | 4,947 | [
"MIT"
] | 1 | 3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 | https://github.com/CV-Rookie/EmbedMask/tree/3b4d9fb4e0b6112dc501708184ff684dfb45f3f0 |
DenseCrossEntropy | import torch
from torch import nn
class DenseCrossEntropy(nn.Module):
""" The CrossEntropy loss that takes the one-hot
vector of the gt label as the input, should be equivalent to the
standard CrossEntropy implementation. The one-hot vector
is meant for the ArcFaceLoss and CutMix augmentation
Ar... | import torch
import triton
import triton.language as tl
from torch._inductor.runtime.triton_heuristics import grid
from torch._C import _cuda_getCurrentRawStream as get_raw_stream
from torch._inductor.runtime import triton_helpers
from torch._inductor.runtime.triton_helpers import math as tl_math
from torch import nn
a... | CTPLab/IID_representation_learning | DenseCrossEntropy | false | 4,948 | [
"MIT"
] | 1 | b9dc13536963f9af332b039f7cc772e2f1090c62 | https://github.com/CTPLab/IID_representation_learning/tree/b9dc13536963f9af332b039f7cc772e2f1090c62 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.